RT-AI-3

'''
Task 1.3.1
idk, something like that maybe
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from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

dot1 = np.array([1.6,1.6,1.6])
dot2 = np.array([2.1,2.1,2.1])
dot3 = np.array([3.4,3.4,3.4])
dot4 = np.array([5.2,5.2,5.2])

print(np.linalg.norm(dot1-dot2-dot3-dot4))

print(np.linalg.norm(dot1-dot2-dot3-dot4)**2)

print(np.linalg.norm(dot1-dot2-dot3-dot4, ord=np.inf))

print(np.linalg.norm(dot1-dot2-dot3-dot4, ord=1))


fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")


ax.scatter(1.6,1.6,1.6)
ax.scatter(2.1,2.1,2.1)
ax.scatter(3.4,3.4,3.4)
ax.scatter(5.2,5.2,5.2)

plt.show()


#Task 2.3.1
from matplotlib import pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
import seaborn as sns

iris = sns.load_dataset('iris')
x_train, x_test, y_train, y_test = train_test_split(iris.iloc[:, :-1],
                                                    iris.iloc[:, -1],
                                                    test_size = 0.20) # просто поменять 0.20 на 0.15 (см второй пункт)

model = KNeighborsClassifier(n_neighbors=3) # поменять 3 на 1/5/10
model.fit(x_train, y_train)
y_pred = model.predict(x_test)

plt.figure(figsize=(10, 7))
sns.scatterplot(x="petal_width",
                y = "petal_length",
                data=iris,
                hue='species',
                s=70)

plt.xlabel("Длина лепестка, см")
plt.ylabel("Ширина лепестка, см")
plt.legend(loc=2)
plt.grid()

for i in range(len(y_test)):
    if np.array(y_test)[i] != y_pred[i]:
        plt.scatter(x_test.iloc[i, 3], x_test.iloc[i, 2], color="red", s=150)

print(y_pred)
print(f'accuracy: {accuracy_score(y_test, y_pred) :.3}')


plt.show()



Task 3.3.2
from sklearn.feature_extraction import DictVectorizer

traits = [{'adult': 3, 'child': 2},
        {'adult': 6, 'child': 4},
        {'adult': 1, 'teen': 3},
        {'adult': 2, 'teen': 1},
        ]


dictvect = DictVectorizer(sparse=False)

traits_matrix = dictvect.fit_transform(traits)

print(traits_matrix)

'''